Strategic Technology Management, Otto Bock Healthcare Products GmbH, Vienna A-1070, Austria.
IEEE Trans Biomed Eng. 2011 Mar;58(3):681-8. doi: 10.1109/TBME.2010.2068298. Epub 2010 Aug 19.
This study presents a novel method for associating features of the surface electromyogram (EMG) recorded from one upper limb to the force produced by the contralateral limb. Bilateral-mirrored contractions from ten able-bodied subjects were recorded along with isometric forces in multiple degrees of freedom (DOF) from the right wrist. An artificial neural network was trained to provide force estimation. Combinations of processing parameters were evaluated and an estimation algorithm allowing high accuracy from relatively short signal epochs (100 ms) was proposed. The estimation performance when using surface EMG from the contralateral limb was 0.90 ± 0.02 for the able-bodied subjects. In comparison, the estimation performance for one subject with congenital malformation of the left forearm was 0.72 which, albeit lower than for able-bodied subjects, is still comparable to or better than previously reported results. The proposed method requires only the measured forces from one limb, such as in the case of unilateral amputees and has thus the potential to be used in clinical settings for intuitive, simultaneous control of multiple DOFs in myoelectric prostheses.
本研究提出了一种新方法，可将从一只上肢记录的表面肌电图（EMG）的特征与对侧肢体产生的力相关联。从十个健康受试者中记录了双侧镜像收缩，并在多个自由度（DOF）中记录了来自右手腕的等长力。训练人工神经网络以提供力估计。评估了处理参数的组合，并提出了一种允许从相对较短的信号时段（100 ms）获得高精度的估计算法。对于健康受试者，使用对侧肢体的表面肌电图进行估计的性能为 0.90 ± 0.02。相比之下，患有左侧前臂先天性畸形的一位受试者的估计性能为 0.72，尽管低于健康受试者，但仍与之前报道的结果相当或更好。该方法仅需要一只肢体的测量力，例如在单侧截肢患者中，因此有可能在临床环境中用于肌电假肢中多个自由度的直观、同步控制。
